Aspect Term Extraction (ATE) identifies aspect terms in review sentences, a key subtask of sentiment analysis. While most existing approaches use energy-intensive deep neural networks (DNNs) for ATE as sequence labeling, this paper proposes a more energy-efficient alternative using Spiking Neural Networks (SNNs). Using sparse activations and event-driven inferences, SNNs capture temporal dependencies between words, making them suitable for ATE. The proposed architecture, SpikeATE, employs ternary spiking neurons and direct spike training fine-tuned with pseudo-gradients. Evaluated on four benchmark SemEval datasets, SpikeATE achieves performance comparable to state-of-the-art DNNs with significantly lower energy consumption. This highlights the use of SNNs as a practical and sustainable choice for ATE tasks.

翻译：方面术语提取（ATE）旨在识别评论句子中的方面术语，是情感分析的关键子任务。现有方法大多使用高能耗的深度神经网络（DNN）将ATE作为序列标注任务处理。本文提出了一种更节能的替代方案，即使用脉冲神经网络（SNN）。通过稀疏激活和事件驱动推理，SNN能够捕捉词语间的时序依赖关系，使其适用于ATE任务。所提出的架构SpikeATE采用三元脉冲神经元和通过伪梯度微调的直接脉冲训练。在四个SemEval基准数据集上的评估表明，SpikeATE在显著降低能耗的同时，取得了与最先进DNN相当的性能。这凸显了SNN作为ATE任务实用且可持续选择的潜力。